Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.

BACKGROUND: The 2019 Novel Coronavirus (COVID-19) is responsible for thousands of deaths and hospitalizations. To curb the spread of this highly transmissible disease, governments enacted protective guidelines for its citizens, including social distancing and stay-at-home orders. These restrictions on social interactions can be especially problematic for individuals managing or recovering from addiction given that treatment often involves access to services and resources that became limited or even unavailable at this time. Social media sites like Twitter serve as a space for users to post questions and concerns about timely topics and allow for researchers to track common themes among the public. The goal of this study was to identify how the public was discussing addiction on Twitter during the COVID pandemic. Methods: We performed a text mining analysis to analyze tweets that contained "addiction" and "covid" to capture posts from the public that illustrated comments and concerns about addiction during the COVID-19 pandemic. We report on 3,301 tweets captured between January 31 and April 23, 2020. The study was conducted in the United States, but contained tweets from multiple countries. Results: The most prevalent topics had to do with services offered by Acadia Healthcare and Serenity Healthcare Centers, attempts to manage time while home, difficulties of coping with alcoholism amidst rising sales of alcohol, and attention to ongoing health crises (e.g.,., opioids, vaping). Additional topics included affordable telehealth services, research from France on the relationship between nicotine and COVID-19, concerns about gambling addiction, and changing patterns in substance misuse as drug availability varies. Conclusions: Analyzing Twitter content enables health professionals to identify the public's concerns about addiction during the COVID-19 pandemic. Findings from text mining studies addressing timely health topics can serve as preliminary analyses for building more comprehensive models, which can then be used to generate recommendations for the larger public and inform policy.

Reactions to foodborne Escherichia coli outbreaks: A text-mining analysis of the public's response.

Foodborne illnesses caused by bacteria are being reported at an increasing rate in the United States. We performed a text-mining analysis to look at nearly 13,000 tweets from two foodborne Escherichia coli outbreaks in 2018. Concerns from the public included staying informed about contaminated lettuce, recognizing signs of infection, and holding responsible farms accountable. At the end of the second outbreak, comments were focused on assessing symptoms, using the traceback process to locate outbreak sources, and calling for better food labeling practices.

A text-mining analysis of the public's reactions to the opioid crisis.

BACKGROUND: Opioid abuse has become an epidemic in the United States. On August 25, 2016, the former Surgeon General of the United States sent an open letter to care providers asking for their help with combatting this growing health crisis. Social media forums like Twitter allow for open discussions among the public and up-to-date exchanges of information about timely topics like opioids. Therefore, the goal of the current study is to identify the public's reactions to the opioid epidemic by identifying the most popular topics tweeted by users. METHODS: We used a text-miner, algorithmic-driven statistical program to capture 73,235 original tweets and retweets posted within a two-month time span (August 15, 2016 through October 15, 2016). All tweets contained references to "opioids," "turnthetide," or similar keywords. We then analyzed the sets of tweets to identify the most prevalent topics. RESULTS: The most discussed topics had to do with public figures addressing opioid abuse, creating better treatment options for teen addicts, using marijuana as an alternative for managing pain, holding foreign and domestic drug makers accountable for the epidemic, promoting the "Rx for Change" campaign, addressing double-standards in the perceptions and treatment of Black and White opioid users, and advertising opioid recovery programs. CONCLUSIONS: Twitter allows users to find current information, voice their concerns, and share calls for action in response to the opioid epidemic. Monitoring the conversations about opioids that are taking place on social media forums like Twitter can help public health officials and care providers better understand how the public is responding to this health crisis.